{
 "cells": [
  {
   "cell_type": "code",
   "id": "ccf0a2c5-6ed1-4b4d-b843-45baef63042c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-10T09:12:17.653222Z",
     "start_time": "2024-10-10T09:11:19.566133Z"
    }
   },
   "source": [
    "import os,sys\n",
    "#dataspath = '/home/qhs/Program/morphGPT/morphGPT/datas/'\n",
    "ROOTDIR = 'D:/deposite/1/morphGPT/'\n",
    "dataspath = ROOTDIR + 'datas/'\n",
    "checkpoint_path = ROOTDIR + 'codes/checkpoints/'\n",
    "#\n",
    "os.chdir(ROOTDIR)\n",
    "\n",
    "from tqdm.notebook import trange, tqdm\n",
    "#\n",
    "import codes.DataProcess as DP\n",
    "import codes.model.ContrasiveRoadNetworkTripChainPretraining as model\n",
    "#\n",
    "import builtins\n",
    "#\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "from importlib import reload"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "osm2gmns, 0.7.6\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "0b047874-dc05-4208-91e5-a5a52f6430d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-09T15:03:03.377613Z",
     "start_time": "2024-10-09T15:03:02.506527Z"
    }
   },
   "source": [
    "device = torch.device('cuda')\n",
    "reload(DP);reload(model)\n",
    "#\n",
    "system_configs = {'dim_embedding_map':8, 'mode_embedding_dim':10, 'num_mode_classification':8,}\n",
    "#\n",
    "#model = tripchaingenerative.to(device)\n",
    "tripchaingenerative = model.GenerativeTripChain_v2(mode_embedding_dim = system_configs['mode_embedding_dim'], \\\n",
    "                                morph_embeding_dim  = system_configs['dim_embedding_map'], \\\n",
    "                                mode_classsification_N = system_configs['num_mode_classification'], decoder_arg_nhead = 13)\n",
    "tripchaingenerative = tripchaingenerative.to(device)\n",
    "checkpoint = torch.load(checkpoint_path + 'model_v2.ckpt')\n",
    "tripchaingenerative.load_state_dict(checkpoint)\n",
    "#--------------------------------Inference, t_MAX_sec is max duration of trip chain\n",
    "GENERATED_TRIP = model.TripGeneration.ContinuousGeneration_using_GenerativeTripChain_v2(model = tripchaingenerative, t_MAX_sec = 3)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\23513\\AppData\\Local\\Temp\\ipykernel_19408\\1470669455.py:11: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  checkpoint = torch.load(checkpoint_path + 'model_v2.ckpt')\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "eedbdbfd-d21c-4f99-b182-a1c48d0d3eae",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-10-09T15:03:05.194048Z",
     "start_time": "2024-10-09T15:03:05.136151Z"
    }
   },
   "source": [
    "#Each element is (t,lon,lat,mode)\n",
    "GENERATED_TRIP"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([0., 0., 0., 5.]),\n",
       " tensor([ 0.0000,  0.0732, -0.0961,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.0937,  0.1255, -0.2101,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.1280,  0.1292, -0.2293,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.1603,  0.1885, -0.3172,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.2446,  0.1991, -0.3769,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.3263,  0.2077, -0.4375,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.4043,  0.2284, -0.4985,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.4569,  0.2364, -0.6169,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.5209,  0.2577, -0.6812,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.5209,  0.2487, -0.7470,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.5412,  0.2995, -0.8563,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.6047,  0.3160, -0.9402,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.6437,  0.3536, -1.0386,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.7154,  0.4031, -1.0784,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.7635,  0.4207, -1.1508,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.8663,  0.4535, -1.2050,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.9442,  0.4756, -1.2607,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 0.9780,  0.5034, -1.3450,  4.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.0535,  0.5438, -1.4508,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.1135,  0.5268, -1.5228,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.2138,  0.5537, -1.6188,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.2689,  0.6019, -1.7201,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.3456,  0.6339, -1.7823,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.3596,  0.6284, -1.8811,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.4118,  0.6319, -1.9521,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.4870,  0.6545, -2.0343,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.5079,  0.6789, -2.0995,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.5728,  0.7042, -2.2045,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.6942,  0.7325, -2.3078,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.7269,  0.7602, -2.3376,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.7324,  0.7833, -2.4153,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.7826,  0.7830, -2.5184,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.8548,  0.8362, -2.6184,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.9301,  0.8670, -2.7238,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 1.9494,  0.8688, -2.8037,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.0483,  0.9018, -2.8932,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.0898,  0.8986, -2.9895,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.1886,  0.9274, -3.0941,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.2359,  0.9540, -3.1969,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.3313,  0.9876, -3.3121,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.3589,  0.9991, -3.4020,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.4241,  1.0456, -3.5032,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.5299,  1.0705, -3.6107,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.6345,  1.1011, -3.7124,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.6345,  1.0834, -3.7470,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.7232,  1.0890, -3.8528,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.7709,  1.1073, -3.9383,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.8474,  1.1337, -4.0447,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.9090,  1.1505, -4.1640,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 2.9981,  1.1804, -4.2591,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>),\n",
       " tensor([ 3.1291,  1.2198, -4.3447,  2.0000], device='cuda:0',\n",
       "        grad_fn=<CatBackward0>)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "2bb41097-9e82-41cd-af95-b674344ac741",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-09T15:03:11.548637Z",
     "start_time": "2024-10-09T15:03:11.377998Z"
    }
   },
   "source": [
    "from codes.draw import *\n",
    "from codes.ui_fun import *"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "ffcfdab5-3595-4b4e-92ae-c29872ffd326",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-10-09T15:03:15.219670Z",
     "start_time": "2024-10-09T15:03:15.208363Z"
    }
   },
   "source": [
    "#无osm底图的gif绘制\n",
    "module = trip_data(GENERATED_TRIP)\n",
    "draw_data = module.gen_df()\n",
    "max_lim = draw_data.max(axis=0).iloc[1:3].to_numpy().astype(float) + 0.03\n",
    "min_lim = draw_data.min(axis=0).iloc[1:3].to_numpy().astype(float) - 0.03"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "18f28fbb-6980-4639-903c-c882d3e11ba4",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-10-09T15:03:42.607133Z",
     "start_time": "2024-10-09T15:03:41.099751Z"
    }
   },
   "source": [
    "import imageio\n",
    "images = []\n",
    "plt.ioff()\n",
    "filename='dongtai.gif'\n",
    "for i in np.linspace(0, draw_data.shape[0] - 2, 10).astype(int) + 1:\n",
    "    temp_data = draw_data.iloc[:i, :]\n",
    "    plot_module = plot_travel_chain_no_osm(temp_data)\n",
    "\n",
    "    fig, ax = plot_module.plot_travel_chain_no_osm(max_lim, min_lim)\n",
    "\n",
    "    buf = BytesIO()\n",
    "    fig.savefig(buf, format='png')\n",
    "    buf.seek(0)\n",
    "    images.append(imageio.imread(buf))\n",
    "imageio.mimsave(filename, images, duration=100, loop=0)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from morphGPT.plot_travel_chain import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n"
   ],
   "id": "eeeb82800e40b21a"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "travel_data_path = \"D://deposite/1\\morphGPT/test/4194160.csv\"\n",
    "column_names = ['id', 'time', 'longitude', 'latitude', 'transport']\n",
    "travel_data = pd.read_csv(travel_data_path,header=None, names=column_names)\n",
    "\n",
    "plot_graph = ox.graph_from_xml(\"D://deposite/1/morphGPT/test/4194160.osm\")\n",
    "\n",
    "plot_module = plot_travel_chain(travel_data,plot_graph)\n",
    "draw_data = plot_module.gene_plot_data()\n",
    "combined_data = np.empty((0, 2))\n",
    "for temp_data in draw_data:\n",
    "    data_points = temp_data[['longitude', 'latitude']].to_numpy()\n",
    "    combined_data = np.vstack((combined_data, data_points))\n",
    "combined_data = np.array(combined_data)"
   ],
   "id": "4a4864e59e242111"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "#以osm作为底图\n",
    "fig,ax = plot_module.plot_travel_chain()\n",
    "\n",
    "\n",
    "# 创建空的点，并设置初始大小（例如：markersize=10）\n",
    "point, = ax.plot([], [], 'bo', markersize=15)  # 动态点的初始位置为空，设置初始大小\n",
    "\n",
    "\n",
    "# 初始化函数，用来设置初始帧\n",
    "def init():\n",
    "    point.set_data([], [])  # 使用空的位置初始化\n",
    "    return point,\n",
    "\n",
    "\n",
    "# 更新函数，每一帧都会调用这个函数\n",
    "def update(frame):\n",
    "    # 设置点的大小（大小可以随帧数动态变化，这里以大小随帧数线性增加为例）\n",
    "    if frame < len(combined_data):\n",
    "        x, y = combined_data[frame, 0], combined_data[frame, 1]\n",
    "        print(f\"Frame {frame}: x={x}, y={y}\")\n",
    "        point.set_data(x, y)  # 更新点位置\n",
    "    return point,\n",
    "\n",
    "\n",
    "# 创建动图\n",
    "ani = FuncAnimation(\n",
    "    fig,\n",
    "    update,\n",
    "    frames=len(combined_data),\n",
    "    init_func=init,\n",
    "    blit=False,\n",
    "    interval=500  # 每帧间隔，单位为毫秒\n",
    ")\n",
    "\n",
    "ani.save('animation.gif', writer='pillow', fps=5)  # 每秒2帧\n",
    "\n",
    "# 展示动图\n",
    "plt.show()"
   ],
   "id": "a5d92b77556eaa94"
  },
  {
   "cell_type": "code",
   "id": "8cefe526-100e-43eb-a47d-1fa5b809ac7f",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-10-10T09:47:45.975268Z",
     "start_time": "2024-10-10T09:15:31.812088Z"
    }
   },
   "source": [
    "from ui_fun import *\n",
    "\n",
    "#以下是界面处理部分\n",
    "if __name__ == \"__main__\":\n",
    "    import tkinter\n",
    "\n",
    "    def delete():  # 删除临时图\n",
    "        root.destroy()\n",
    "        gif.close()\n",
    "\n",
    "    def show(file_name,label,root):\n",
    "        gif = playGif(file_name)\n",
    "        gif.playGif(root, label)  # 实现动态插放\n",
    "\n",
    "\n",
    "    def gen_g(file_name):\n",
    "        gif = playGif(file_name)\n",
    "        return gif\n",
    "\n",
    "\n",
    "    root = tkinter.Tk()\n",
    "    root.geometry('680x360')\n",
    "    root.title('动态出行链生成')\n",
    "\n",
    "    #上部菜单栏设计\n",
    "    # 菜单\n",
    "    menubar = tkinter.Menu(root)\n",
    "\n",
    "\n",
    "    def show_frame(frame):\n",
    "        frame.tkraise()\n",
    "\n",
    "    # 子菜单\n",
    "    submenu = tkinter.Menu(menubar, activebackground='blue')\n",
    "    submenu.add_command(label='gif display', command=lambda: show_frame(frame1))\n",
    "    submenu.add_command(label='gif on osm', command=lambda: show_frame(frame2))\n",
    "    # 把子菜单添加到主菜单中\n",
    "    menubar.add_cascade(label='Menu', menu=submenu)\n",
    "    menubar.add_command(label='Quit', command=root.destroy)\n",
    "    # 把主菜单配置到窗体中\n",
    "    root.config(menu=menubar)\n",
    "\n",
    "    #设计两个次级窗口\n",
    "    frame1 = tkinter.Frame(root)\n",
    "    frame2 = tkinter.Frame(root)\n",
    "\n",
    "    for frame in (frame1, frame2):\n",
    "        frame.place(x=0, y=0, width=900, height=420)\n",
    "\n",
    "    '''\n",
    "    针对第一窗口，即无osm底图的出现链进行展示\n",
    "    '''\n",
    "\n",
    "    label11 = tkinter.Label(frame1,text=\"动图展示区域\",\n",
    "                            fg='red',\n",
    "                            font=('华文新魏',32)\n",
    "                          )\n",
    "\n",
    "    label11.place(x=300, y=0, width=600, height=420)\n",
    "\n",
    "    #文本输入界面\n",
    "    entry1 = tkinter.Entry(frame1)  # 设置文本输入框的宽度\n",
    "    entry1.place(x=20, y=70, width=240, height=30)\n",
    "    file = ROOTDIR+ \"dongtai.gif\"\n",
    "\n",
    "\n",
    "    #提示文本\n",
    "    label12 = tkinter.Label(frame1, text='''\n",
    "            输入绝对路径后\n",
    "            点击generate生成动态图\n",
    "                                      ''',\n",
    "                          fg='black',\n",
    "                          font=('华文新魏', 10)\n",
    "                          )\n",
    "    label12.place(x=20, y=30, width=240, height=30)\n",
    "\n",
    "    #生成按钮\n",
    "    button1 = tkinter.Button(frame1, text=\"generate\", command=lambda: show(file,label11,frame1),\n",
    "                             font = 120,\n",
    "                             bg='#d3fbfb',\n",
    "                             width=20,\n",
    "                             height=2\n",
    "                             )\n",
    "    button1.place(x=40, y=180, width=150, height=50)\n",
    "    root.protocol('WM_DELETE_WINDOW', delete)  #####【重要】关闭窗口后的事件：delete\n",
    "\n",
    "    '''\n",
    "     针对第二窗口，即存在osm底图的出现链进行展示\n",
    "     '''\n",
    "\n",
    "    label21 = tkinter.Label(frame2, text=\"动图展示区域\",\n",
    "                            fg='red',\n",
    "                            font=('华文新魏', 32)\n",
    "                            )\n",
    "\n",
    "    label21.place(x=300, y=0, width=600, height=420)\n",
    "\n",
    "    # 文本输入界面\n",
    "    entry2 = tkinter.Entry(frame2)  # 设置文本输入框的宽度\n",
    "    entry2.place(x=20, y=70, width=240, height=30)\n",
    "    file2 = ROOTDIR+ \"animation.gif\"\n",
    "\n",
    "    # 提示文本\n",
    "    label22 = tkinter.Label(frame2, text='''\n",
    "             输入osm的底图\n",
    "             点击generate生成以osm为底的动态图\n",
    "                                       ''',\n",
    "                            fg='black',\n",
    "                            font=('华文新魏', 10)\n",
    "                            )\n",
    "    label22.place(x=20, y=30, width=240, height=30)\n",
    "\n",
    "    # 生成按钮\n",
    "    button2 = tkinter.Button(frame2, text=\"generate\", command=lambda: show(file2, label21, frame2),\n",
    "                             font=120,\n",
    "                             bg='#d3fbfb',\n",
    "                             width=20,\n",
    "                             height=2\n",
    "                             )\n",
    "    button2.place(x=40, y=180, width=150, height=50)\n",
    "    root.protocol('WM_DELETE_WINDOW', delete)  #####【重要】关闭窗口后的事件：delete\n",
    "\n",
    "\n",
    "    #开始时展示第一个界面\n",
    "    show_frame(frame1)\n",
    "\n",
    "    root.mainloop()\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in Tkinter callback\n",
      "Traceback (most recent call last):\n",
      "  File \"D:\\Environment\\Envs\\python3.10.6\\lib\\tkinter\\__init__.py\", line 1921, in __call__\n",
      "    return self.func(*args)\n",
      "  File \"C:\\Users\\23513\\AppData\\Local\\Temp\\ipykernel_12344\\1106573618.py\", line 9, in delete\n",
      "    gif.close()\n",
      "NameError: name 'gif' is not defined\n"
     ]
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   "id": "121e79c2-41da-40ac-80e6-cdd7cbc0d830",
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   "outputs": [],
   "source": []
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